基于动态径流系数和城市功能区的Storm Water Management Model 参数率定方法
Calibration method of an Storm Water Management Model based ondynamic runoff coefficients and urban functional areas
投稿时间:2023-10-19  修订日期:2024-03-12
DOI:
中文关键词:  径流系数  SWMM模型  城市功能区  不确定性参数  Horton方程
英文关键词:Runoff coefficient  SWMM model  Urban functional area  Uncertain parameters  Horton equation
基金项目:华东院科技项目(ZXY2021-FJ-02-08);水利部重大科技项目(SKR-2022056)
作者单位邮编
邵银龙 华东勘测设计院福建有限公司 350003
李晓晨 中国水利水电科学研究院 
廖美廷 华东勘测设计院福建有限公司 
马景胜 华东勘测设计院福建有限公司 
白音包力皋* 中国水利水电科学研究院 100038
周小日 华东勘测设计院福建有限公司 
徐艳秋 华东勘测设计院福建有限公司 
梁艳慧 华东勘测设计院福建有限公司 
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中文摘要:
      城市雨洪模型是研究城市内涝形成规律及演进过程的重要手段,但在我国城市化进程加速、雨水内涝监测能力不足的背景下,模型参数校准和应用面临挑战。为解决缺乏实测雨洪数据条件下城市雨洪模型参数校准的难题,本文提出了根据地理和气候特征计算雨水径流量的动态径流系数法和基于城市功能区的SWMM参数校准方法。在福建省三明市的应用表明:动态径流系数法与规范和经验公式结果一致,与传统方法相比则能反映降雨产流随雨强、下渗等因素变化的规律,更符合城市降雨产流的实际过程。基于城市功能区的参数校准方法结果与研究区城市化水平和下垫面特征相符。校准后雨水径流过程NSE值达到0.80,雨水总径流量误差处于6%以内,洪峰时间误差小于3分钟。本文提出的方法可为缺乏实测雨洪数据地区的城市雨洪模拟提供参考。
英文摘要:
      Urban stormwater models are essential in analyzing the patterns and dynamics of urban flooding. However, calibrating and applying these models presents challenges amid China's rapid urbanization and limited stormwater monitoring infrastructure. To overcome the challenges of parameter calibration in urban stormwater models without empirical stormwater data, this study employs a dynamic runoff coefficient method for estimating stormwater runoff volumes, employing Sanming city, Fujian Province, as a case study. Moreover, we introduce a SWMM parameter calibration strategy based on urban functional areas to enhance model accuracy and reliability. The application reveals that the dynamic runoff coefficient method corresponds closely with conventional and empirically derived formulas, effectively capturing the variability of runoff coefficients in response to rainfall intensity and infiltration changes. This tailored parameter calibration method for urban functional areas produces results in harmony with the area's level of urbanization and surface characteristics. The optimized model successfully achieves an NSE value of 0.80, keeping total runoff volume error within 6% and peak flow timing error under three minutes. The methodologies proposed herein offer valuable insights for simulating urban stormwater in regions without comprehensive stormwater data.
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